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Making the sire selection process simpler: A web-based decision support tool (iGENDEC)

Matt Spangler for Progressive Cattle Published on 24 February 2020

Sire selection requires identifying a breeding objective; choosing a breed or (preferably) breeds, based on objective differences; choosing a seedstock supplier and then choosing a bull. This requires knowledge of production environments, firm-level economics, breed differences, heterosis and genetic predictions (e.g., expected progeny difference or EPD).

It is challenging, if not impossible, to have a profit-motivated cattle operation without using “modern” genetic selection tools. Admittedly, condensing data into information from which informed decisions can be made deserves more attention to enable cattle producers to better utilize proven technology.

The need

Bull purchasing decisions need to account for differing marketing goals and environmental constraints to improve profitability and sustainability, but these are unique to each herd as producer-specific production goals and inputs vary considerably. For instance, it is well known that calving ease is more important when considering bulls that will be mated to heifers than it is when selecting bulls to be mated to mature cows. Calving ease is also more important in herds that have high levels of dystocia or that calve in extensive range environments than in herds with infrequent dystocia or readily available labor at calving. Additionally, in low-input environments, selection for decreased mature size and lower milk production levels are advantageous if heifers are to be produced from within the herd.

These are examples where inputs, defined as either labor or feedstuff availability, dictate optimal production levels. The targeted market endpoint also dictates traits and production levels that are economically relevant at the individual firm level. For producers who market all calves towards a quality grid (e.g., Certified Angus Beef target) without retaining replacements, survivability, disease susceptibility, sale weight and carcass quality are primary economic drivers, and traits such as weaning weight (direct and maternal) are irrelevant.

Knowledge of the value of individual bulls available, and the value differences amongst them, would greatly enhance the profitability of commercial cow-calf enterprises by allowing selection decisions to focus on what is economically important and what bull price is justified to achieve the subsequent goals for a particular firm given its resource constraints. Without the aid of a decision support tool, commercial beef cattle producers are forced to attempt to combine several disjointed pieces of information (e.g., current herd performance, EPD of potential seedstock, accuracy of EPD, mean breed differences, projected costs and value of production, production environment constraints, etc.) to decide which bull to buy and to determine the economic value conditional on their own needs.

Producers face the problem of obtaining the best bulls for their operation in that given setting. It is worth noting here that “best” is a relative concept. When accounting for price differentials across bulls, a “less desirable” bull may become the preferred choice over a “more desirable” bull if his sale price discount is larger than the differential in value between the two bulls. Conversely, if the spread in bull prices does not sufficiently reflect the differences in economic value of the bulls offered, having good estimates of value should increase profitability of top seedstock producers. Furthermore, customized indices open the opportunity for different customers to rank bulls differently, which would also increase profitability of seedstock producers.

Current work

Of the multiple-trait selection methods available (tandem selection, independent culling levels and economic selection indices), economic selection indices are clearly the preferred method. Unfortunately, they are largely misunderstood and underutilized. In 2018, a team including scientists from the University of Nebraska-Lincoln, Kansas State University, the U.S. Meat Animal Research Center and Theta Solutions LLC, were awarded a USDA grant. Their fundamental objective is to develop and provide software (iGENDEC) that enables beef producers to make more profitable genetic selection decisions, integrating genetic effects (EPD and heterosis), available resources and firm-level economics.

Currently, we have framed three possible use cases: commercial buyers (genetic purchasing decisions based on firm-specific breeding objectives), seedstock sellers (matching sale offering to individual customers) and seedstock buyers (matching genetic purchasing decisions to specified goals). For any of these cases, the user would identify a set of candidates for selection, which may include bulls currently in service for reference and possible replacements. The user would also enter information about their operation and cow herd in order to determine the appropriate selection index. Beyond that, more advanced users would have opportunity to provide more detailed information, such as costs of available grazed and harvested feed resources, herd-specific labor requirements and costs, and carcass grid premiums and discounts that will enable derivation of more fully customized selection indices.

Specifying breed composition of the cowherd will enable comparison of candidate bulls to reflect differences in heterosis expected in the progeny as well as differences due to EPD. Ultimately, this provides the user with a list of bulls, across breeds, with estimates of the economic value each would bring to a given operation. This allows the user to have an ordered list that reports the differences in net profit between candidate bulls.

The impetus for this project is not the belief that currently available selection indices are so inherently flawed they are of little value. Rather, our motivation is that selection decisions can be improved. Part of this improvement is simply encouraging beef cattle producers to utilize proven tools (e.g., selection indices), and we believe allowing beef cattle producers to take part in the creation of their own selection index has the potential to increase the rate of technology adoption. It is important to note that producers who have more detailed firm-level data (e.g., unit cost of production) will benefit more from customizable indices. This project ends in 2021, at which time our goal is to have a web-based tool that can be utilized by the U.S. beef cattle industry.  end mark

Matt Spangler
  • Matt Spangler

  • Beef Genetics Extension Specialist
  • University of Nebraska – Lincoln Extension
  • Email Matt Spangler

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